An Information Roadmap Method for Robotic Sensor Path Planning

  • Authors:

  • G. Zhang;S. Ferrari;M. Qian

  • Affiliations:

  • Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA 27708;Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA 27708 and Department of Electrical and Computer Engineering, Duke University, Durham, USA 27708;Department of Electrical and Computer Engineering, Duke University, Durham, USA 27708

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2009

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Abstract

A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor's platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.